adaptive safety-critical control

Addressing the challenge of achieving both high performance and formal safety guarantees for high-dimensional autonomous systems in real-world environments, this paper proposes a two-stage co-optimization framework. In the first stage, state constraints are relaxed into penalty terms within a gradient-based model predictive control (MPC) formulation, enhancing computational efficiency and scalability. In the second stage, a safety-critical control barrier function (CBF)-based filter is constructed and implemented via quadratic programming (QP) to minimally modify a reference controller while strictly enforcing hard safety constraints. The method innovatively integrates gradient optimization, relaxed safety-constrained optimal control problems (SC-OCPs), and CBF-QP filtering—thereby reconciling high-performance control with formal safety certification, while avoiding the excessive conservatism and computational infeasibility common in conventional safety filters. The approach is validated on two high-dimensional, complex dynamical systems.

This work addresses the gap between theoretical safety guarantees and practical feasibility of Control Barrier Functions (CBFs) in real-world systems subject to input constraints, where implicit assumptions often render CBFs ineffective. By systematically distinguishing between candidate and valid CBFs, the study uncovers the true source of safety in passive systems and extends safety verification to non-passive systems. Integrating system dynamics, explicit input constraint modeling, and class-K function analysis, the authors establish precise conditions under which CBFs yield valid safety assurances in low-dimensional systems and derive actionable design principles for safe controllers. An accompanying interactive web platform visually illustrates the core mechanisms and common pitfalls, offering practitioners an intuitive guide for reliable deployment.

This work addresses the challenge of safely transferring safety guarantees between heterogeneous systems with mismatched dynamics by proposing a transfer Control Barrier Function (tCBF) framework. The approach systematically migrates safety constraints from a source system to a target system by integrating a simulation function with an explicit margin term, which compensates for model mismatch. Safety is enforced via a quadratic programming-based safety filter that minimally modifies the nominal control input. Notably, this method achieves cross-system safety certificate transfer without requiring assumptions on matching state dimensions or dynamical structures. The explicit margin ensures robustness against model discrepancies, thereby preserving safety in the target system. The efficacy of tCBF is demonstrated in a quadrotor obstacle avoidance task, where safety constraints are successfully transferred with negligible interference to the original controller, highlighting the framework’s generality and practical utility.

This work addresses the challenge of simultaneously ensuring safety and compliance in human–robot interaction under dynamic uncertainties, external forces, and actuator saturation. To this end, an online adaptive impedance control framework is proposed, featuring a novel position–velocity composite nonsmooth control barrier function that unifies the handling of relative-degree-one safety constraints. Real-time compliant interaction is achieved through a quadratic programming-based safety filter. Unknown dynamics are compensated online using an interval type-2 fuzzy system, while a soft-constraint mechanism with exact penalty recovery mitigates torque saturation effects. Theoretical analysis establishes forward invariance of the safe set and uniform ultimate boundedness of tracking errors. Experimental validation on a 7-degree-of-freedom robotic manipulator demonstrates robust and safe impedance control performance under significant model uncertainty and external disturbances.

This paper addresses the safety-critical control problem for nonlinear systems subject to parametric uncertainties. We propose a novel safety-adaptive control framework that explicitly incorporates a barrier state (BaS) into the system dynamics. By augmenting the plant model with the BaS and co-designing a control Lyapunov function with an adaptive law, we achieve tight coupling between safety constraints and online parameter learning—ensuring hard satisfaction of safety boundaries even under unknown dynamics. Rigorous analysis establishes global stability and safety of the closed-loop system. The framework is validated on two benchmark scenarios: planar quadrotor control (with unknown aerodynamic drag) and adaptive cruise control. Compared to state-of-the-art methods, our approach reduces safety boundary violation rates by over 80% and decreases tracking error by approximately 35%.

This work addresses the challenge of online reinforcement learning under strict safety constraints while maintaining smooth learning dynamics. To this end, the authors propose AutoSafe, a novel architecture that seamlessly integrates structured safety monitoring and intervention mechanisms directly into the policy action generation process. By leveraging a risk-aware behavior-switching mechanism, AutoSafe achieves a continuous and adaptive trade-off between performance optimization and safety assurance. The approach combines structured safe policy composition with an online learning framework for continuous control, effectively circumventing the discontinuities typically introduced by conventional intervention strategies. Empirical results demonstrate that AutoSafe simultaneously attains high safety constraint satisfaction rates and smooth learning dynamics across multiple continuous control benchmarks, with successful real-world deployment validated on a physical inverted pendulum system.

This work addresses the limitation of traditional safety mechanisms, which rely solely on state predicates and struggle to enforce smoothness constraints on higher-order derivatives such as velocity and acceleration. To overcome this, the paper proposes a higher-order safety shield synthesis method grounded in finite-state safety games. By employing finite differences to formally encode differential safety properties within a discrete state space, the problem is transformed into a safety game requiring memory of past states. The authors theoretically establish that enforcing a k-th order property necessitates preserving exactly k steps of history, and leverage this insight to design a layered, iterative algorithm for synthesizing maximally permissive strategies. This approach substantially reduces the search space, enhances synthesis efficiency, and effectively supports runtime enforcement of multi-order physical constraints.

This work addresses the challenge of ensuring safe and autonomous robot navigation in complex dynamic environments by proposing a novel “Control Barrier Corridor” framework. It unifies control barrier functions with safety corridors for the first time, reformulating safety constraints as locally feasible target regions. By integrating feedback control with convex optimization, the method generates reference trajectories that guarantee continuous safety in real time. The approach is validated on fully actuated systems, unicycle models, and linear output regulation systems, demonstrating its broad applicability. A key contribution lies in establishing a tunable trade-off between safety and responsiveness, enabling verifiably safe, persistent, and adaptive exploration even in unknown environments.

To address the challenge of online adaptive control for safety-critical autonomous systems operating in uncertain environments, this paper proposes a safety-constrained online learning framework. Methodologically, it integrates optimal control, parameter adaptive estimation, and extended Kalman filtering, and innovatively introduces a softplus barrier function to embed safety constraints in an initial-condition-independent manner—rigorously proving both convergence and formal safety guarantees. The framework enables efficient, robust, control-guided online learning. Experimental validation on inverted pendulum and robotic arm tasks demonstrates significant improvements: approximately 40% higher data efficiency and a safety constraint satisfaction rate of 99.2%, substantially outperforming baseline approaches. Crucially, it eliminates the strong reliance on an initially safe policy inherent in conventional methods. This work establishes a verifiable, deployable paradigm for safety-driven autonomous learning.

This work addresses the challenge of achieving both high performance and provable safety for autonomous systems operating in real-world environments, where conventional model predictive control (MPC) often fails to guarantee safety beyond the finite prediction horizon. The authors propose a novel approach that constructs terminal constraints using a safety value function derived from reachability analysis, ensuring that the planned trajectory terminates within a controlled invariant safe set. This formulation guarantees recursive feasibility while enabling real-time, provably safe trajectory optimization with high task performance. In contrast to existing methods that rely on local linearization or overly conservative approximations, the proposed technique significantly reduces conservatism and enhances expressiveness of safety guarantees. Simulations and hardware experiments on a Flexiv Rizon 10s robotic arm demonstrate that the method substantially improves constraint satisfaction and robustness compared to standard MPC and reactive safety filters, without compromising task performance.